Machine Learning in Drug Development: Opportunities and Challenges

Abstract

Machine learning (ML) is transforming the landscape of drug development by accelerating drug discovery, improving clinical trial efficiency, and enhancing predictive accuracy. This article explores the opportunities ML presents in drug development, from identifying novel drug targets to optimizing clinical trial designs. However, significant challenges remain, including data quality issues, model interpretability, and regulatory hurdles. Despite these challenges, the integration of ML in drug development holds great promise for revolutionizing the pharmaceutical industry. This article provides an overview of current ML applications, addresses key challenges, and discusses future directions for ML in drug development.